16,398 research outputs found
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European Heart Rhythm Association (EHRA)/Heart Rhythm Society (HRS)/Asia Pacific Heart Rhythm Society (APHRS)/Latin American Heart Rhythm Society (LAHRS) Expert Consensus Statement on the state of genetic testing for cardiac diseases.
RELATIONSHIP BETWEEN BODY-SEAT INTERFACE PRESSURE AND DISCOMFORT DURING ROWING
Discomfort and pressure-related tissue injury to the buttocks are common complaints among rowers. The soft tissues of the buttocks are non-uniformly loaded during rowing. The current state of literature on seating discomfort is inconclusive as to a desirable body-seat interface pressure pattern. The purpose of this study was to determine whether localising pressure under bony protuberances or diffusing pressure over soft tissues would result in the least amount of discomfort. Force sensing arrays were used to measure body-seat interface pressures in 11 elite female rowers during rowing. Peak pressure measures were identified and pressure gradients were calculated. Discomfort was quantified using a questionnaire, and pressure data were then correlated with discomfort scores.Discomfort was weakly correlated with each of maximal pressure gradient (r=0.45) and peak pressure (r=0.43). The findings indicate pressure should be redistributed in order to avoid concentrating pressure under the bony protuberances o f the buttocks
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NOAH-H, a deep-learning, terrain classification system for Mars: Results for the ExoMars Rover candidate landing sites
In this investigation a deep learning terrain classification system, the âNovelty or Anomaly Hunter â HiRISEâ (NOAH-H), was used to classify High Resolution Imaging Science Experiment (HiRISE) images of Oxia Planum and Mawrth Vallis. A set of ontological classes was developed that covered the variety of surface textures and aeolian bedforms present at both sites. Labelled type-examples of these classes were used to train a Deep Neural Network (DNN) to perform semantic segmentation in order to identify these classes in further HiRISE images.
This contribution discusses the methods and results of the study from a geomorphologists perspective, providing a case study applying machine learning to a landscape classification task. Our aim is to highlight considerations about how to compile training datasets, select ontological classes, and understand what such systems can and cannot do. We highlight issues that arise when adapting a traditional planetary mapping workflow to the production of training data. We discuss both the pixel scale accuracy of the model, and how qualitative factors can influence the reliability and usability of the output.
We conclude that âlandscape levelâ reliability is critical for the use of the output raster by humans. The output can often be more useful than pixel scale accuracy statistics would suggest, however the product must be treated with caution, and not considered a final arbiter of geological origin. A good understanding of how and why the model classifies different landscape features is vital to interpreting it reliably. When used appropriately the classified raster provides a good indication of the prevalence and distribution of different terrain types, and informs our understanding of the study areas. We thus conclude that it is fit for purpose, and suitable for use in further work
Genome-wide identification of the genetic basis of amyotrophic lateral sclerosis
Amyotrophic lateral sclerosis (ALS) is a complex disease that leads to motor neuron death. Despite heritability estimates of 52%, genome-wide association studies (GWASs) have discovered relatively few loci. We developed a machine learning approach called RefMap, which integrates functional genomics with GWAS summary statistics for gene discovery. With transcriptomic and epigenetic profiling of motor neurons derived from induced pluripotent stem cells (iPSCs), RefMap identified 690 ALS-associated genes that represent a 5-fold increase in recovered heritability. Extensive conservation, transcriptome, network, and rare variant analyses demonstrated the functional significance of candidate genes in healthy and diseased motor neurons and brain tissues. Genetic convergence between common and rare variation highlighted KANK1 as a new ALS gene. Reproducing KANK1 patient mutations in human neurons led to neurotoxicity and demonstrated that TDP-43 mislocalization, a hallmark pathology of ALS, is downstream of axonal dysfunction. RefMap can be readily applied to other complex diseases
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Privacy-aware Smart Home Interface Framework
Smart home user interfaces are pervasive and shared by multiple users who occupy the space. Therefore, they pose a risk to interpersonal privacy of occupants because an individualâs sensitive information can be leaked to other co-occupants (information privacy), or they can be disturbed by intrusions into their personal space (physical privacy) when the co-occupant interacts with the smart home user interfaces. This thesis hypothesises that interpersonal privacy violations can be mitigated by adapting the user interface layer and presents insights into how to achieve usable user interface adaptation to mitigate or minimise interpersonal privacy violations in smart homes.
The thesis reports two case studies and two user studies. The first case study identifies the key characteristics needed to model the rich context of interpersonal privacy violations scenarios. Then it presents knowledge representation models that are required to represent the identified characteristics and evaluates them for adequacy in modelling the context information of interpersonal privacy violation scenarios. The second case study presents a software architecture and a set of algorithms that can detect interpersonal privacy violations and generate usable user interface adaptations. Then it evaluates the architecture and the algorithms for adequacy in generating usable privacy-aware user interface adaptations. The first user study (N=15) evaluates the usability of the adaptive user interfaces generated from the framework where storyboards were used as the stimulant. Extending the findings from the usability study and expanding the coverage of example scenarios, the second user study (N=23) evaluates the overall user experience of the adaptive user interfaces, using video prototypes as the stimulant.
The research demonstrates that the characteristics identified, and the respective knowledge representation models adequately captured the context of interpersonal privacy violation scenarios. Furthermore, the software architecture and the algorithms could detect possible interpersonal privacy violations and generate usable user interface adaptations to mitigate them. The two user studies demonstrate that the adaptive user interfaces, when used in appropriate situations, were a suitable solution for addressing interpersonal privacy violations while providing high usability and a positive user experience. The thesis concludes by providing recommendations for developing privacy-aware user interface adaptations and suggesting future work that can extend this research
Vagus Nerve Stimulation in Medically- Resistant Epilepsy: Efficacy and Tolerance
Background: Epilepsy is a common neurological disease that affects 1% of the population. One
third of patients with epilepsy will not respond to antiseizure medications. The most effective
treatment when a patient has medically resistant epilepsy is epilepsy surgery. Unfortunately, in
many cases surgery is not possible. Neuromodulation is a therapy used in those patients and
Vagus Nerve Stimulation (VNS) is the most common type. There are many studies focusing on
seizure reduction using VNS, it is still unclear which patients will obtain the greatest benefits.
Objective: To define the seizure response post-VNS implantation, to determine predictive
factors associated with good outcomes after VNS implantation and to evaluate complications
and side effects. Analysis will be completed in the total sample of VNS cases, in the paediatric
subgroup, in medically resistant generalized epilepsy and pregnant women implanted with VNS.
Patients & Methods: Patients with medically resistant epilepsy implanted with VNS at the
London Health Science Centre-Western University, from 1997 to July 2018.
Results: 1) VNS in epilepsy: 114 patients were included. Median seizure rate reduction was -
67.8% and 55.6% (n=41) had a âĽ50% seizure reduction. There was a reduction of hospitalization
after VNS implantation from 89.5% (n=102) to 45.6% (n=52). 5.3% (n=6) developed side effects
associated with the implantation; and side effects were reported in 63.2% (n=72). 2) Paediatric
Group: 22 patients were included. The median age when the VNS was implanted was 13. A âĽ50%
seizure reduction was achieved in 50% (n=11) and the median seizure reduction was -75%. Side
effects were detected in 54.5% (n=12). 3) 46 patients were included in this study with a history
of medically resistant generalized epilepsy. The mean age at implantation was 24 years-old. Of
the LGS group 41.7% (n=12) of patients had an overall seizure reduction of âĽ50%, and in the GGE
group 64.7% (n=11) had a seizure reduction of âĽ50%. There was a significant reduction of
seizure-related hospital admissions. 4) Four patients and seven pregnancies were included. The
median duration since implantation was 3.17 years. Three required c-sections, one related to
failure to progress, the second due to pre-eclampsia and the third due to breach presentation.
All babies were healthy, except one with developmental delay of unclear severity.
Conclusion: 1) VNS can reduce the number of seizures by 50% in more than half of the patients
implanted. VNS has shown a reduction in hospitalization. It is a safe therapy with frequent mild
side effects. 2) The paediatric population obtained similar results compared to the total sample.
3) VNS should be considered as a treatment in patients with therapy resistant generalized
epilepsy, especially in cases with GGE. 4) Our small sample suggests VNS is a relatively safe
therapy during pregnancy, however, larger sample series should be collected
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National Emergencies Trust Coronavirus Appeal evaluation. Phase 1 report
Facial expression recognition and intensity estimation.
Doctoral Degree. University of KwaZulu-Natal, Durban.Facial Expression is one of the profound non-verbal channels through which human emotion state is inferred from the deformation or movement of face components when facial muscles are activated. Facial Expression Recognition (FER) is one of the relevant research fields in Computer Vision (CV) and Human-Computer Interraction (HCI). Its application is not limited to: robotics, game, medical, education, security and marketing. FER consists of a wealth of information. Categorising the information into primary emotion states only limit its performance. This thesis considers investigating an approach that simultaneously predicts the emotional state of facial expression images and the corresponding degree of intensity. The task also extends to resolving FER ambiguous nature and annotation inconsistencies with a label distribution learning method that considers correlation among data. We first proposed a multi-label approach for FER and its intensity estimation using advanced machine learning techniques. According to our findings, this approach has not been considered for emotion and intensity estimation in the field before. The approach used problem transformation to present FER as a multilabel task, such that every facial expression image has unique emotion information alongside the corresponding degree of intensity at which the emotion is displayed. A Convolutional Neural Network (CNN) with a sigmoid function at the final layer is the classifier for the model. The model termed ML-CNN (Multilabel Convolutional Neural Network) successfully achieve concurrent prediction of emotion and intensity estimation. ML-CNN prediction is challenged with overfitting and intraclass and interclass variations. We employ Visual Geometric Graphics-16 (VGG-16) pretrained network to resolve the overfitting challenge and the aggregation of island loss and binary cross-entropy loss to minimise the effect of intraclass and interclass variations. The enhanced ML-CNN model shows promising results and outstanding performance than other standard multilabel algorithms. Finally, we approach data annotation inconsistency and ambiguity in FER data using isomap manifold learning with Graph Convolutional Networks (GCN). The GCN uses the distance along the isomap manifold as the edge weight, which appropriately models the similarity between adjacent nodes for emotion predictions. The proposed method produces a promising result in comparison with the state-of-the-art methods.Author's List of Publication is on page xi of this thesis
Foot and Ankle Impairments Affecting Mobility in Stroke
Introduction:
Altered foot characteristics are common in people with stroke, with a third presenting with abnormal foot posture which is associated with ambulatory difficulties. Understanding the relationship between measures of foot and ankle impairment and their association with mobility and balance outcomes is therefore important; however, poor clinimetric properties of foot and ankle measures after stroke precludes evaluation of these relationships. Therefore, this research, undertaken as part of a multicentred research project, had the following aims:
Study 1: To evaluate the clinimetric properties (feasibility, testâretest reliability, and clinical relevance) of measures of foot and ankle impairments, for application in people with stroke.
Study 2: To examine how these measures differ between people with stroke and normal controls; and whether they are associated with mobility and balance outcomes.
Methods:
In Study 1, community-dwelling people with stroke, able to walk 10 m (metres), attended two testing sessions to evaluate the clinimetric properties of different foot and ankle measures. These included: static foot posture and dynamic foot loading (peak plantar pressure, PPP, contact area, CA and centre of pressure, CP) using a plantar pressure mat; isometric muscle strength using a hand-held dynamometer (HHD); peak ankle and hallux dorsiflexion and stiffness using bespoke rigs; and ankle plantarflexion spasticity using the Tardieu scale. Statistical analysis used intraclass correlation coefficients (ICCsââ,ââ), standard error of measurement (SEM) and BlandâAltman plots.
In Study 2, measures identified as reliable from Study 1 were incorporated in a cross-sectional study design. Participants were recruited from acute and community neurological services in East London and North Devon. Statistical analysis tested the differences between groups and between affected limbs in people with stroke. Impairment measures were evaluated using multivariate regression analysis for their association with functional outcomes: walking speed (over 10 m); Timed Up and Go (TUAG), Forward Functional Reach Test (FFRT) and presence of falls (> 1 in the last 3 months).
Results:
In Study 1, 21 people with stroke tested the measures. These were found to be feasible and easy to administer, although loss of data (up to 33%) was observed. All measures had moderate to excellent testâretest reliability (coefficients 0.50â0.98), except ankle plantarflexion stiffness (ICCsââ,ââ = 0.00â0.11).
In Study 2, there were significant differences in all measures between people with stroke (n = 180) and controls (n = 46), apart from static foot posture (p = 0.670), toe deformity (p = 0.782) and peak hallux dorsiflexion (p = 0.320). Between limb differences were identified for all measures except foot posture (p = 0.489) and foot CA (p > 0.05). Multicollinearity analysis found 10 measures appropriate for multivariate regression which identified the following R² and variance explained: 59% walking speed (R² = 0.543); 49% TUAG (R² = 0.435); 36% FFRT (R² = 0.285) and 26% for Falls Presence.
Conclusion:
The study demonstrated that seven foot and ankle measures of impairment after stroke were clinically feasible, reliable and associated with mobility and balance outcomes. The measures were ankle and foot isometric muscle strength, sway velocity, PPP (RFT and FFT), CA (MFT and FFT) and peak ankle dorsiflexion. These measures can now be incorporated into research to examine methods to improve the treatment of foot and ankle after stroke
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